How much is Universal Accessibility Actually Taught in Canadian Occupational Therapy Programs?
Bibliographic record
Abstract
Background. The environment is essential to occupational participation. However, the involvement and training of occupational therapists in universal accessibility (UA) seems limited. Purpose. To explore the content on UA taught in university occupational therapy programs in Canada. Method. This study adopts a mixed methodology. A survey, including mostly quantitative data, was distributed to occupational therapy programs across Canada, and course syllabi related to UA, providing qualitative data, were requested. Analysis included descriptive statistics and content descriptive analysis. Findings. Thirteen out of 14 programs responded to the survey and five provided course syllabi. While all programs cover UA content, only seven offer specific courses on accessibility. Internships related to UA are offered by seven programs. Some gaps identified include limited knowledge, lack of in-depth content, and limited interprofessional collaboration. There are variations in emphasis on UA and teaching approaches. Boundaries between universal approaches and knowledge of specific disability groups are poorly delineated. UA is unanimously considered essential in the training of occupational therapists. Conclusion. The content in UA is heterogeneous across programs. Consensual definitions of accessibility concepts related to occupational therapy are needed to better define the role of the occupational therapist in this area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".